Evaluating quality of annotation

ABSTRACT

A method for evaluating annotation quality is provided. The method may include obtaining annotation information associated with a plurality of annotators and a plurality of data elements including a plurality of annotation entries corresponding to at least one data element and entered based on an annotation guideline, determining a quality rating for the annotation guideline based on a comparison between a first value associated with the plurality of annotators and the plurality of data elements and a second value associated with any disparity among the plurality of annotation entries, determining a proficiency rating for an annotator from the plurality of annotators based on a comparison between a third value associated with annotation entries by the annotator and the second value, and generating a report based on the quality rating and the proficiency rating.

BACKGROUND

The present invention relates to evaluating quality of an annotation.

In corpus construction, annotations may be manually added in order givea semantic structure to a text document. A plurality of annotators mayadd annotations according to an annotation guideline. Recently, varioustechniques have been known regarding evaluating quality of anannotation.

SUMMARY

According to one embodiment, a method for evaluating annotation qualityis provided. The method may include obtaining annotation informationassociated with a plurality of annotators and a plurality of dataelements including a plurality of annotation entries corresponding to atleast one data element from the plurality of data elements and enteredbased on an annotation guideline, determining a quality rating for theannotation guideline based on a comparison between a first valueassociated with the plurality of annotators and the plurality of dataelements and a second value associated with any disparity among theplurality of annotation entries, determining a proficiency rating for anannotator from the plurality of annotators based on a comparison betweena third value associated with annotation entries by the annotator andthe second value associated with any disparity among the plurality ofannotation entries, and generating a report based on the quality ratingand the proficiency rating.

According to another embodiment, a computer program product forevaluating annotation quality is provided. The computer program productmay include at least one computer readable non-transitory storage mediumhaving computer readable program instructions for execution by aprocessor. The computer readable program instructions may includeinstructions for obtaining annotation information associated with aplurality of annotators and a plurality of data elements including aplurality of annotation entries corresponding to at least one dataelement from the plurality of data elements and entered based on anannotation guideline, determining a quality rating for the annotationguideline based on a comparison between a first value associated withthe plurality of annotators and the plurality of data elements and asecond value associated with any disparity among the plurality ofannotation entries, determining a proficiency rating for an annotatorfrom the plurality of annotators based on a comparison between a thirdvalue associated with annotation entries by the annotator and the secondvalue associated with any disparity among the plurality of annotationentries, and generating a report based on the quality rating and theproficiency rating.

According to another embodiment, a computer system for evaluatingannotation quality is provided. The system may include at least oneprocessing unit, at least one computer readable memory, at least onecomputer readable tangible, non-transitory storage medium, and programinstructions stored on the at least one computer readable tangible,non-transitory storage medium for execution by the at least oneprocessing unit via the at least one computer readable memory. Theprogram instructions may include instructions for obtaining annotationinformation associated with a plurality of annotators and a plurality ofdata elements including a plurality of annotation entries correspondingto at least one data element from the plurality of data elements andentered based on an annotation guideline, determining a quality ratingfor the annotation guideline based on a comparison between a first valueassociated with the plurality of annotators and the plurality of dataelements and a second value associated with any disparity among theplurality of annotation entries, determining a proficiency rating for anannotator from the plurality of annotators based on a comparison betweena third value associated with annotation entries by the annotator andthe second value associated with any disparity among the plurality ofannotation entries, and generating a report based on the quality ratingand the proficiency rating.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a block diagram of a computer system according to anexemplary embodiment of the present invention.

FIG. 2 depicts an example of a hardware configuration of a computerdevice according to an exemplary embodiment of the present invention.

FIG. 3 depicts the first example of evaluation processing according toan exemplary embodiment of the present invention.

FIG. 4 depicts the second example of evaluation processing according toan exemplary embodiment of the present invention.

FIG. 5 depicts a flowchart representing an example of an operationperformed by a computer device according to an exemplary embodiment ofthe present invention.

DETAILED DESCRIPTION

Hereinafter, an exemplary embodiment of the present invention will bedescribed in detail with reference to the attached drawings.

It is to be noted that the present invention is not limited to thisexemplary embodiment to be given below and may be implemented withvarious modifications within the scope of the present invention. Inaddition, the drawings used herein are for purposes of illustration, andmay not show actual dimensions.

For example, to give a semantic structure to a document in corpusconstruction, annotators may add annotations to document elementsconstituting the document. The annotations may be categories of thedocument elements. The document elements may include words, phrases, andsentences. Thus, an annotation “Person” is assumed to be added to a word“Lincoln”. Meanwhile, the annotators may be persons who are responsiblefor adding the annotations to the document elements. In this exemplaryembodiment, the annotators are assumed to add the annotations to thedocument elements according to an annotation guideline as one example ofa guideline. The annotation guideline may establish standards regardingwhat kinds of annotations are to be added to what kinds of documentelements.

If an annotation has low quality, this exemplary embodiment may detectwhether it is because the annotation guideline has low quality, orbecause any of the annotators has low proficiency. In addition, if anyof the annotators has low proficiency, this exemplary embodiment maydetect which annotator has low proficiency.

Referring to FIG. 1, there is shown a block diagram of a computer system1 to which the exemplary embodiment is applied. As shown in the figure,the computer system 1 may include a storage device 10 and a computerdevice 20 connected to each other via a communication line 30.

The storage device 10 may include a document storage 120. The documentstorage 120 may store document data of documents, such as newspapers,technical papers, or the like. As described above, the document data mayinclude plural document elements. An annotation may be added to any ofthe plural document elements. Hereinafter, the document elements arealso referred to as “tokens”. Although the exemplary embodiment takes asan example a case where the document data is stored and the annotationsare added to the document elements, the case may be generalized to acase where general data is stored and the annotations are added to dataelements included in the general data.

The computer device 20 may include an information obtaining module 220,an evaluation module 240, and an information output module 260.

The information obtaining module 220 may obtain annotation informationas one example of first information. The annotation information mayindicate which annotator has added which annotation to which token. Forexample, the information obtaining module 220 may generate theannotation information by analyzing the document stored in the documentstorage 120. The annotation information may be of any type (e.g., tabletype information).

The evaluation module 240 may evaluate an annotation based on theannotation information obtained by the information obtaining module 220,if notified that the annotation has low quality as measured by, forexample, Inter-Annotator Agreement (IAA). The evaluation module 240 maydetermine whether the evaluation result shows, as one example of a firstevaluation result, that low quality of the annotation guideline hascaused the low quality of the annotation, or the evaluation resultshows, as one example of a second evaluation result, that lowproficiency of any of the annotators has caused the low quality of theannotation. If the evaluation result shows that the low proficiency ofany of the annotators has caused the low quality of the annotation, theevaluation module 240 may further determine that the evaluation resultshows, as one example of a third evaluation result, that low proficiencyof a specific annotator has caused the low quality of the annotation.The details of this determination will be described later. If theevaluation result shows that the low quality of the annotation guidelinehas caused the low quality of the annotation, the evaluation module 240may generate support information for supporting revision of theannotation guideline to cause the annotation guideline to have higherquality with respect to annotating. If the evaluation result shows thatthe low proficiency of a specific annotator has caused the low qualityof the annotation, the evaluation module 240 may generate supportinformation for supporting training of the specific annotator to causethe specific annotator to have higher proficiency with respect toannotating.

The information output module 260 may output the evaluation result bythe evaluation module 240, as one example of second information. If theevaluation result shows that the low quality of the annotation guidelinehas caused the low quality of the annotation, the evaluation module 240may output information to that effect and the support information forsupporting revision of the annotation guideline. If the evaluationresult shows that the low proficiency of the specific annotator hascaused the low quality of the annotation, the evaluation module 240 mayoutput information to that effect and the support information forsupporting training of the specific annotator.

Referring to FIG. 2, there is shown an example of a hardwareconfiguration of the computer device 20 in the exemplary embodiment. Asshown in the figure, the computer device 20 may include a centralprocessing unit (CPU) 21 serving as one example of a processor, a mainmemory 22 connected to the CPU 21 via a motherboard (M/B) chip set 23and serving as one example of a memory, and a display driver 24connected to the CPU 21 via the same M/B chip set 23. A networkinterface 26, magnetic disk device 27, audio driver 28, andkeyboard/mouse 29 are also connected to the M/B chip set 23 via a bridgecircuit 25.

In FIG. 2, the various configurational elements are connected via buses.For example, the CPU 21 and the M/B chip set 23, and the M/B chip set 23and the main memory 22 are connected via CPU buses, respectively. Also,the M/B chip set 23 and the display driver 24 may be connected via anaccelerated graphics port (AGP). However, when the display driver 24includes a PCI express-compatible video card, the M/B chip set 23 andthe video card are connected via a PCI express (PCIe) bus. Also, whenthe network interface 26 is connected to the bridge circuit 25, a PCIExpress may be used for the connection, for example. For connecting themagnetic disk device 27 to the bridge circuit 25, a serial AT attachment(ATA), a parallel-transmission ATA, or peripheral componentsinterconnect (PCI) may be used. For connecting the keyboard/mouse 29 tothe bridge circuit 25, a universal serial bus (USB) may be used.

Referring to FIG. 3, there is shown the first example of evaluationprocessing. As shown in the figure, an annotation information table 221,annotation type tables 222 a, 222 b, . . . , and score tables 223 a, 223b, . . . may be generated in the main memory 22 during the evaluationprocessing. In the first example, the tokens are assumed to be“Lincoln”, “lived”, “in”, “the”, “white”, and “house”, and theannotators are assumed to be A, B, C, D, and E. Further, in the firstexample, the annotation types are assumed to be “Organization”,“Location”, “Person”, and “None” (indicating that no annotation isadded).

First, the information obtaining module 220 may generate the annotationinformation table 221. As shown in the figure, the annotationinformation table 221 may show that the annotators A and B have addedthe annotation “Organization” to the tokens “white” and “house”, andthat the annotators C, D, and E have added the annotation “Person” tothe token “Lincoln” and the annotation “Location” to the tokens “white”and “house”.

Next, the evaluation module 240 may generate, for each of the annotationtypes, corresponding one of the annotation type tables 222 a, 222 b, . .. from the annotation information table 221. For example, an annotationtype table 222 a may be generated for the annotation type“Organization”, and an annotation type table 222 b may be generated forthe annotation type “Location”.

Subsequently, the evaluation module 240 may generate the score tables223 a, 223 b, . . . from the annotation type tables 222 a, 222 b, . . ., respectively. For example, a score table 223 a may be generated fromthe annotation type table 222 a, and a score table 223 b may begenerated from the annotation type table 222 b. Explanations are madebelow regarding the evaluating processing, taking as an example a casewhere the score table 223 a is generated from the annotation type table222 a.

The evaluation module 240 may divide the annotators A to E, for eachtoken, into two groups including a group of one or more annotators whohave added the annotation “Organization” to the token and a group of oneor more annotators who have not added the annotation “Organization” tothe token. Note that, if no annotator has added the annotation“Organization” to the token or all annotators A to E have added theannotation “Organization” to the token, the annotators A to E may beincluded in one group while no annotator may be included in the othergroup. Then, the evaluation module 240 may store “1” in one or morecells in the score table 223 a, each corresponding to an annotatorbelonging to a group (hereinafter referred to as “majority”) includingthe majority of the annotators in the two groups, and store “0” in oneor more cells in the score table 223 a, each corresponding to anannotator belonging to the other group (hereinafter referred to as“minority”) including the minority of the annotators in the two groups.If the number of annotators who have added the annotation “Organization”to the token and the number of annotators who have not added theannotation “Organization” to the token are equal, the evaluation module240 may store “1” in one or more cells in the score table 223 a, eachcorresponding to an annotator belonging to any one of the two groups.

After that, the evaluation module 240 may calculate a TokenScoreindicating a score for each token by using the following formula:TokenScore=(the number of annotators belonging to the majority for thetoken)−(the number of annotators belonging to the minority for thetoken). If the number of annotators belonging to the majority for thetoken and the number of annotators belonging to the minority for thetoken are equal, the evaluation module 240 may set the TokenScore to 0.For each of the tokens “Lincoln”, “lived”, “in”, and “the”, “1” isstored in five cells and “0” is stored in no cell. Thus, the TokenScorefor each of the tokens “Lincoln”, “lived”, “in”, and “the” is set to 5(from the second row to the fifth row in the rightmost column of thescore table 223 a). For each of the tokens “white” and “house”, “1” isstored in three cells and “0” is stored in two cells. Thus, theTokenScore for each of the tokens “white” and “house” is set to 1 (thesixth row and the seventh row in the rightmost column of the score table223 a).

Further, the evaluation module 240 may calculate a GuidelineScoreindicating a score of an annotation guideline by using the followingformula: GuidelineScore=(the total sum of TokenScores)/((the number ofannotators)*(the number of tokens)). The GuidelineScore may take a valuefrom 0 to 1. For example, in the score table 223 a, the TokenScores forthe tokens “Lincoln”, “lived”, “in”, “the”, “white”, and “house” are 5,5, 5, 5, 1, and 1, respectively, and the numbers of annotators andtokens are 5 and 6, respectively. Thus, the GuidelineScore is set to22/30 (the lowermost row in the rightmost column of the score table 223a). However, the foregoing formula is one example, and any formula maybe used if it calculates a score by comparing, for each token, thenumber of annotators who have added the annotation to the token and thenumber of annotators who have not added the annotation to the token.

Furthermore, the evaluation module 240 may calculate an AnnotatorScoreindicating a score for each annotator by using the following formula:AnnotatorScore=(the total sum of TokenScores of tokens for which theannotator belongs to the majority)/(the total sum of TokenScores).Specifically, the evaluation module 240 may first calculate anAnnotatorBaseScore indicating a base score of the annotator by using thefollowing formula: AnnotatorBaseScore=(the total sum of TokenScores oftokens for which the annotator belongs to the majority)/((the number ofannotators)*(the number of tokens)). Next, the evaluation module 240 maynormalize the AnnotatorBaseScore by dividing the AnnotatorBaseScore bythe GuidelineScore to obtain the AnnotatorScore. The AnnotatorScore maytake a value from 0 to 1, and this enables to set a threshold by using aproportion. For example, in the score table 223 a, the tokens for whichthe annotator A belongs to the majority are “Lincoln”, “lived”, “in”,and “the”, and the TokenScores for these tokens are all 5. In addition,the total sum of the TokenScores is 22. Thus, the AnnotatorScore of theannotator A is set to 20/22 (the lowermost row in the columncorresponding to the annotator A of the score table 223 a). However, theforegoing formula is one example, and any formula may be used if itcalculates a score by comparing, for each token, whether or not theannotator has added the annotation to the token and whether or not amajority of the annotators has added the annotation to the token.

After that, the evaluation module 240 may determine whether or not theannotation guideline has low quality. Specifically, the evaluationmodule 240 may determine whether or not the GuidelineScore is greaterthan or equal to a threshold for GuidelineScores (e.g., 0.8). If theGuidelineScore is greater than or equal to the threshold, the evaluationmodule 240 may determine that the annotation guideline has high quality.On the contrary, if GuidelineScore is smaller than the threshold, theevaluation module 240 may determine that the annotation guideline haslow quality.

Further, the evaluation module 240 may determine whether or not anannotator has low proficiency. Specifically, the evaluation module 240may determine whether or not the AnnotatorScore of the annotator isgreater than or equal to a threshold for AnnotatorScores (e.g., 0.8). Ifthe AnnotatorScore is greater than or equal to the threshold, theevaluation module 240 may determine that the annotator has highproficiency. On the contrary, if the AnnotatorScore is smaller than thethreshold, the evaluation module 240 may determine that the annotatorhas low proficiency.

Note that the aforementioned thresholds may be set by a project manageror the like as fixed values in accordance with experiences in pastprojects and quality required in the present project.

In the first example, as for the score table 223 a, the evaluationmodule 240 may determine that the GuidelineScore (22/30=0.73) is smallerthan the threshold for GuidelineScores (0.8). Therefore, the evaluationmodule 240 may determine that the annotation guideline has low qualityregarding the annotation type “Organization”. As a result, theinformation output module 260 may output information indicating that theannotation guideline has low quality.

Furthermore, in the first example, the evaluation module 240 may supportrevision of the annotation guideline. If the annotation guideline hasbeen determined to have low quality, the evaluation module 240 mayprovide such support, for each annotation type.

The evaluation module 240 may support revision of the annotationguideline in two manners.

In the first manner, the evaluation module 240 may specify one or moretokens corresponding to annotating by any of the annotators belonging tothe minority. Concerning each of the one or more tokens, the evaluationmodule 240 may count, for each of the annotation types, the number ofannotators who have added the annotation of the annotation type. Forexample, in the annotation type tables 222 a and 222 b, the tokenscorresponding to annotating by any of the annotators belonging to theminority are “white” and “house”. Thus, concerning each of the tokens“white” and “house”, the evaluation module 240 may count the number ofannotators who have added the annotation “Organization” to find that thenumber is two, and count the number of annotators who have added theannotation “Location” to find that the number is three. As a result, theinformation output module 260 may output information about these countsfor each of the one or more tokens to an editor or the like of theannotation guideline.

If the number of tokens corresponding to annotating by any of theannotators belonging to the minority is large, the information outputmodule 260 may output information about such counts only for each tokensatisfying the following condition: (TokenScore for the token)/(thenumber of annotators)<(the threshold for GuidelineScores). Satisfyingthis condition indicates that the TokenScore for the token is relativelylow, namely that annotations of various annotation types have been addedto the token.

In the second manner, the evaluation module 240 may specify one or moreeasily-confused pairs of annotation types. Specifically, the evaluationmodule 240 may assume a TokenScore for a token as an element of aninteger vector, for each of the two annotation types. The evaluationmodule 240 may calculate similarity between the integer vector for oneannotation type and the integer vector for the other annotation type.The cosine value of an angle made by the two integer vectors may be usedto represent the similarity between the two integer vectors. Theevaluation module 240 may specify an easily-confused pair of theannotation types based on the similarity between the two integervectors. For example, in the score tables 223 a and 223 b, both of theinteger vector for the annotation type “Organization” and the integervector for the annotation type “Location” are vectors (5, 5, 5, 5, 1,1), and the cosine value is 1. As a result, the information outputmodule 260 may output information about easily-confused pairs ofannotation types to an editor or the like of the annotation guideline.

Referring to FIG. 4, there is shown the second example of evaluationprocessing. As shown in the figure, an annotation information table 226,annotation type tables 227 a, 227 b, . . . , and score tables 228 a, 228b, . . . may be generated in the main memory 22 during the evaluationprocessing. Also in the second example, the tokens are assumed to be“Lincoln”, “lived”, “in”, “the”, “white”, and “house”, and theannotators are assumed to be A, B, C, D, and E. Further, in the secondexample, the annotation types are assumed to be “Person”, “Location”,and “None” (indicating that no annotation is added).

First, the information obtaining module 220 may generate the annotationinformation table 226. As shown in the figure, the annotationinformation table 226 may show that the annotators A has added theannotation “Person” to the tokens “white” and “house”, and that theannotators B, C, D, and E have added the annotation “Person” to thetoken “Lincoln” and the annotation “Location” to the tokens “white” and“house”.

Next, the evaluation module 240 may generate, for each of the annotationtypes, corresponding one of the annotation type tables 227 a, 227 b, . .. from the annotation information table 226. For example, an annotationtype table 227 a may be generated for the annotation type “Person”, andan annotation type table 227 b may be generated for the annotation type“Location”.

Subsequently, the evaluation module 240 may generate the score tables228 a, 228 b, . . . from the annotation type tables 227 a, 227 b, . . ., respectively. For example, a score table 228 a may be generated fromthe annotation type table 227 a, and a score table 228 b may begenerated from the annotation type table 227 b. Explanations are madebelow regarding the evaluating processing, taking as an example a casewhere the score table 228 a is generated from the annotation type table227 a.

The evaluation module 240 may divide the annotators A to E, for eachtoken, into two groups including a group of one or more annotators whohave added the annotation “Person” to the token and a group of one ormore annotators who have not added the annotation “Person” to the token.Note that, if no annotator has added the annotation “Person” to thetoken or all annotators A to E have added the annotation “Person” to thetoken, the annotators A to E may be included in one group while noannotator may be included in the other group. Then, the evaluationmodule 240 may store “1” in one or more cells in the score table 228 a,each corresponding to an annotator belonging to the majority of the twogroups, and store “0” in one or more cells in the score table 228 a,each corresponding to an annotator belonging to the minority of the twogroups. If the number of annotators who have added the annotation“Person” to the token and the number of annotators who have not addedthe annotation “Person” to the token are equal, the evaluation module240 may store “1” in one or more cells in the score table 228 a, eachcorresponding to an annotator belonging to any one of the two groups.

After that, the evaluation module 240 may calculate a TokenScoreindicating a score for each token, in the same manner as in the firstexample. For each of the tokens “Lincoln”, “white”, and “house”, “1” isstored in four cells and “0” is stored in one cell. Thus, the TokenScorefor each of the tokens “Lincoln”, “white”, and “house” is set to 3 (thesecond row, the sixth row and the seventh row in the rightmost column ofthe score table 228 a). For each of the tokens “lived”, “in”, and “the”,“1” is stored in five cells and “0” is stored in no cell. Thus, theTokenScore for each of the tokens “lived”, “in”, and “the” is set to 5(from the third row to the fifth row in the rightmost column of thescore table 228 a).

Further, the evaluation module 240 may calculate a GuidelineScoreindicating a score of an annotation guideline, in the same manner as inthe first example. For example, in the score table 228 a, theTokenScores for the tokens “Lincoln”, “lived”, “in”, “the”, “white”, and“house” are 3, 5, 5, 5, 3, and 3, respectively, and the numbers ofannotators and tokens are 5 and 6, respectively. Thus, theGuidelineScore is set to 24/30 (the lowermost row in the rightmostcolumn of the score table 228 a).

Furthermore, the evaluation module 240 may calculate an AnnotatorScoreindicating a score for each annotator, in the same manner as in thefirst example. For example, in the score table 228 a, the tokens forwhich the annotator A belongs to the majority are “lived”, “in”, and“the”, and the TokenScores for these tokens are all 5. In addition, thetotal sum of the TokenScores is 24. Thus, the AnnotatorScore of theannotator A is set to 15/24 (the lowermost row in the columncorresponding to the annotator A of the score table 228 a).

After that, the evaluation module 240 may determine whether or not theannotation guideline has low quality. Specifically, the evaluationmodule 240 may determine whether or not the GuidelineScore is greaterthan or equal to a threshold for GuidelineScores (e.g., 0.8). If theGuidelineScore is greater than or equal to the threshold, the evaluationmodule 240 may determine that the annotation guideline has high quality.On the contrary, if the GuidelineScore is smaller than the threshold,the evaluation module 240 may determine that the annotation guidelinehas low quality.

Further, the evaluation module 240 may determine whether or not anannotator has low proficiency. Specifically, the evaluation module 240may determine whether or not the AnnotatorScore of the annotator isgreater than or equal to a threshold for AnnotatorScores (e.g., 0.8). Ifthe AnnotatorScore is greater than or equal to the threshold, theevaluation module 240 may determine that the annotator has highproficiency. On the contrary, if the AnnotatorScore is smaller than thethreshold, the evaluation module 240 may determine that the annotatorhas low proficiency.

In the second example, as for the score table 228 a, the evaluationmodule 240 may determine that the GuidelineScore (24/30=0.8) is equal tothe threshold for GuidelineScores (0.8). Therefore, the evaluationmodule 240 may determine that the annotation guideline has high qualityregarding the annotation type “Person”. Further, the evaluation module240 may determine that the AnnotatorScore of the annotator A(15/24=0.625) is smaller than the threshold for AnnotatorScores (0.8).Therefore, the evaluation module 240 may determine that the annotator Ahas low proficiency regarding the annotation type “Person”. As a result,the information output module 260 may output information indicating thatthe annotation guideline has high quality and the annotator A has lowproficiency.

Furthermore, in the second example, the evaluation module 240 maysupport training of one or more annotators having low proficiency(hereinafter also referred to as “unskilled annotators”). If anannotator has been determined to be unskilled, the evaluation module 240may provide such support to the unskilled annotator, for each annotationtype.

The evaluation module 240 may specify one or more tokens correspondingto annotating by the unskilled annotator belonging to the minority.Concerning each of the one or more tokens, the evaluation module 240 maycheck all annotation type tables 227 a, 227 b, . . . , and specify anannotation type the annotation of which has been added by annotatorsbelonging to the majority. For example, in the annotation type tables227 a and 227 b, the tokens corresponding to annotating by the annotatorA belonging to the minority are “white” and “house”. Thus, concerningeach of the tokens “white” and “house”, the evaluation module 240 mayspecify the annotation type “Location” as an annotation type theannotation of which has been added by annotators belonging to themajority. As a result, the information output module 260 may outputinformation about the specified annotation type for each of the one ormore tokens to the unskilled annotator or the like.

Referring to FIG. 5, there is shown a flowchart representing an exampleof an operation performed by the computer device 20. In the foregoingdescription, the operation is assumed to start when notified that theannotation has low quality as measured by the IAA. However, in thefollowing description, the operation is assumed to start even when notnotified that the annotation has low quality as measured by the IAA. Forexample, the operation may start when an operation request is receivedor a predetermined timing is reached.

When the operation starts in the computer device 20, the informationobtaining module 220 may obtain annotation information based on documentdata stored in the document storage 120 (step 201). For example, theinformation obtaining module 220 may generate the annotation informationtable (e.g., 221 or 226) by analyzing the document data.

Next, the evaluation module 240 may determine whether or not allannotation types have been processed (step 202). If all annotation typeshave not been processed, the evaluation module 240 may select anannotation type from annotation types which have not been processed(step 203). After that, the evaluation module 240 may calculate a scoreof the annotation guideline and scores of the annotators for theselected annotation type (step 204). For example, the evaluation module240 may calculate the GuidelineScore indicating a score of theannotation guideline and AnntatorScores indicating scores of theannotators, as described above with reference to FIGS. 3 and 4.

Thus, the evaluation module 240 may determine whether or not a score ofthe annotation guideline is low (step 205). For example, the evaluationmodule 240 may determine whether or not the GuidelineScore is greaterthan or equal to the threshold for GuidelineScores. If the score of theannotation guideline is low, the evaluation module 240 may generatesupport information for supporting revision of the annotation guideline(step 206). For example, the evaluation module 240 may generateinformation indicating the number of annotators who have added theannotation of each annotation type to the tokens corresponding toannotating by any of the annotators belonging to the minority.Alternatively, the evaluation module 240 may generate informationindicating easily-confused pairs of annotation types. After that, theinformation output module 260 may output information indicating that theannotation guideline has low quality, and the support informationgenerated at step 206 (step 207).

If, at step 205, the score of the annotation guideline is not low, theevaluation module 240 may determine whether or not a score of anyannotator is low (step 208). For example, the evaluation module 240 maydetermine whether or not any AnotatorScore is greater than or equal tothe threshold for AnotatorScore. If the score of a specific annotator islow, the evaluation module 240 may generate support information forsupporting training of the specific annotator (step 209). For example,the evaluation module 240 may generate information indicating anannotation type the annotation of which has been added by annotatorsbelonging to the majority to each of the tokens corresponding toannotating by the specific annotator belonging to the minority. Afterthat, the information output module 260 may output informationindicating that the specific annotator has low proficiency, and thesupport information generated at step 209 (step 210).

The operation above is repeated until all annotation types areprocessed. If, at step 202, all annotation types have been processed,the operation ends.

In one embodiment, a method for evaluating annotation quality mayinclude obtaining annotation information associated with a plurality ofannotators and a plurality of data elements (e.g., tokens, words in asentence). The annotation information may include a plurality ofannotation entries corresponding to at least one data element from theplurality of data elements, and the plurality of annotation entries maybe entered based on an annotation guideline. The method may also includedetermining a quality rating for the annotation guideline, and thedetermining may be based on a comparison between a first valueassociated with the plurality of annotators and the plurality of dataelements and a second value associated with any disparity among theplurality of annotation entries. In one example, a disparity may includedifferences between types of annotation entries (e.g., “Organization”versus “Location”, as depicted in FIG. 3, annotation information table221) and whether an annotation has been entered or not (e.g., “Person”versus “NONE”, as depicted in FIG. 3, annotation information table 221).The method may also include determining a proficiency rating for anannotator from the plurality of annotators, and the determining may bebased on a comparison between a third value associated with annotationentries by the annotator and the second value associated with anydisparity among the plurality of annotation entries. The method may alsoinclude generating a report based on the quality rating and theproficiency rating.

In an embodiment, the generated report may indicate whether the qualityrating is sufficient or insufficient based on a threshold value and/orwhether the proficiency rating is sufficient or insufficient based onanother threshold value. In an embodiment, a sufficient quality ratingor a sufficient proficiency rating may lead to a minimal or blank resultprovided in the generated report. In another embodiment, the report maybe generated based on a determination that the quality rating and/orproficiency rating is below a threshold value. In an embodiment, basedon a determination that the quality rating is below a threshold, thegenerated report may include information supporting revision to theannotation guideline, which may be used to improve the annotationguideline by correcting aspects of the annotation guideline associatedwith the poor quality rating. In an embodiment, based on a determinationthat the proficiency rating is below a threshold, the generated reportmay include information supporting training for a particular annotator,which may be used to improve the annotation proficiency for theparticular annotator.

In one embodiment, the method may further include determining whether anannotation has been entered for each data element from the plurality ofdata elements by each annotator from the plurality of annotators. In oneexample, with reference to annotation information table 221 (depicted inFIG. 3), the value “NONE” indicates that various data elements (e.g.,tokens) do not have any annotation entered by a particular annotator.

In a further embodiment, the method may include generating a pluralityof annotation type tables based on the plurality of annotation entries.The plurality of annotation entries includes multiple types ofannotation entries, and each of the plurality of annotation type tablescorresponds to one of the multiple types of annotation entries. In oneexample, the annotation type tables may resemble annotation type tables222 a, 222 b, depicted in FIG. 3. The method may also include generatinga plurality of score tables based on the plurality of annotation typetables including a plurality of element scores, and each element scorefrom the plurality of element scores is associated with a particulardata element from the plurality of data elements and is based on whetheran annotation has been entered for the particular data element by eachannotator from the plurality of annotators. In one example, the scoretables may resemble score tables 223 a, 223 b, depicted in FIG. 3.

In one embodiment, determining the quality rating comprises determininga ratio between the first value and the second value, and whereindetermining the proficiency rating comprises determining a ratio betweenthe second value and the third value. In a further embodiment, the firstvalue, the second value, and the third value are based on the pluralityof score tables. In one example, with reference to score table 223 a,depicted in FIG. 3, the quality rating may be represented by a fractionin the bottom row in the rightmost cell with the first value being thedenominator and the second value being the numerator, and theproficiency rating for annotator “A” may be represented by a fraction inthe bottom row of the “A” column with the second value being thedenominator and the third value being the numerator.

In a further embodiment, each score table from the plurality of scoretables includes a plurality of cells corresponding to a first dataelement and each annotator from the plurality of annotators, each cellfrom the plurality of cells has a value based on whether an annotationhas been entered for the first data element by each annotator from theplurality of annotators, and the element score for the first dataelement is based on a difference between a number of cells from theplurality of cells having a first cell value and a number of cells fromthe plurality of cells having a second cell value. A difference mayinclude a difference between types of annotation entries (e.g.,“Organization” versus “Location”, as depicted in FIG. 3, annotationinformation table 221) and/or a difference with respect to an annotationhaving been entered or not (e.g., “Person” versus “NONE”, as depicted inFIG. 3, annotation information table 221). In one embodiment, the firstcell value may be associated with a majority of cells corresponding tothe first data element and the second cell value may be associated witha minority of cells corresponding to the first data element.

In one embodiment, the method may further include determining thequality rating is below a threshold value, and generating informationassociated with the quality rating supporting revisions to theannotation guideline. Such information may include data elements (e.g.,tokens) that may have insufficient annotation quality due to poorguidance from the annotation guideline. Such information may be includedin the generated report.

In one embodiment, the method may further include determining theproficiency rating for a particular annotator is below a thresholdvalue, and generating information associated with proficiency ratingsupporting training for the particular annotator. Such information maybe included in the generated report.

In another embodiment, a method for evaluating annotation quality mayinclude obtaining annotation information (e.g., a first information)indicating whether or not each of a plurality of annotators has addedthe annotation to each of a plurality of data elements, and outputtingother information (e.g., a second information) indicating at leasteither one of a first evaluation result and a second evaluation result,the first evaluation result showing that a guideline for annotating haslow quality with respect to annotating, the second evaluation resultshowing that any of the plurality of annotators has low proficiency withrespect to annotating.

In further embodiments, the method may further include obtaining thefirst evaluation result by comparing, for each data element of theplurality of data elements, a number of one or more annotators who haveadded the annotation to the each data element and a number of one ormore annotators who have not added the annotation to the each dataelement. The method may further include outputting information forsupporting revision of the guideline to cause the guideline to havehigher quality with respect to annotating, in a case where the secondinformation indicates the first evaluation result.

In further embodiments, the second information may indicate a thirdevaluation result showing that a specific annotator of the plurality ofannotators has low proficiency, in a case where the second informationindicates the second evaluation result. The method may further includeobtaining the third evaluation result by comparing, for each dataelement of the plurality of data elements, whether or not the specificannotator has added the annotation to the each data element and whetheror not a majority of the plurality of annotators has added theannotation to the each data element. The method may further includeoutputting information for supporting training of the specific annotatorto cause the specific annotator to have higher proficiency with respectto annotating, in a case where the second information indicates thesecond evaluation result.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. This invention may, however, be embodied inmany different forms and should not be construed as limited to theexemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope of this invention to thoseskilled in the art. In the description, details of well-known featuresand techniques may be omitted to avoid unnecessarily obscuring thepresented embodiments.

References in the specification to “one embodiment”, “an embodiment”,“an example embodiment”, etc., indicate that the embodiment describedmay include a particular feature, structure, or characteristic, butevery embodiment may not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, when a particular feature,structure, or characteristic is described in connection with anembodiment, it is submitted that it is within the knowledge of oneskilled in the art to affect such feature, structure, or characteristicin connection with other embodiments whether or not explicitlydescribed.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method for evaluating annotation quality, the method comprising: obtaining annotation information associated with a plurality of annotators and a plurality of data elements, wherein the annotation information comprises a plurality of annotation entries corresponding to at least one data element from the plurality of data elements, and wherein the plurality of annotation entries are entered based on an annotation guideline; determining a quality rating for the annotation guideline, wherein the determining is based on a comparison between a first value associated with the plurality of annotators and the plurality of data elements and a second value associated with any disparity among the plurality of annotation entries, wherein determining the quality rating comprises determining a ratio between the first value and the second value, and wherein determining the proficiency rating comprises determining a ratio between the second value and the third value; determining a proficiency rating for an annotator from the plurality of annotators, wherein the determining is based on a comparison between a third value associated with annotation entries by the annotator and the second value associated with any disparity among the plurality of annotation entries; generating a report based on the quality rating and the proficiency rating; determining whether an annotation has been entered for each data element from the plurality of data elements by each annotator from the plurality of annotators. generating a plurality of annotation type tables based on the plurality of annotation entries, wherein the plurality of annotation entries comprises multiple types of annotation entries, and wherein each of the plurality of annotation type tables corresponds to one of the multiple types of annotation entries; generating a plurality of score tables based on the plurality of annotation type tables comprising a plurality of element scores, wherein each element score from the plurality of element scores is associated with a particular data element from the plurality of data elements and is based on whether an annotation has been entered for the particular data element by each annotator from the plurality of annotators; 